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As climate changes, regional responses may become more apparent; impacts often can become natural hazards, adversely affecting millions of people, on all continents and most nations. The coupling of hazards to climate change scenarios is a great challenge of climate change science. Nevertheless, it is extremely important to observe, simulate and ultimately understand this coupling, for the benefit of society and sustainability of the Earth's environment. Different applied mathematical techniques have been used to discern real effects of these changes and study long term trends. Moreover, those techniques can be applied in addressing the intensity and frequency of extreme events associated with climate change at regional scales and would be an important step in facing future extreme events associated with climate change. Computational methods include applying statistical data analysis, mesoscale and climate simulations while assisting modeling efforts with satellite based observations. Predictive analytics platforms would be very useful for assessing impacts of climate variability and change on the frequency and intensity of extreme events and how these extreme events can affect water and air quality issues globally. These tools are an innovative technology that applies data mining methods, predictive models, analysis and reporting to data, without the inherent limitations of current On Line Analytical Processing tools that suffer from cube rigidity, database explosion and dimensional constriction. Climate-induced changes are complex and vary across a wide range of dimensions. An important part of predictive analytics platforms are their unlimited dimensionality and the segmentation of data by 'physical' or 'performance' characteristics. For example, a physical dimension might be the climate divisions in the state of California. A performance dimension could be the arithmetical, mathematical, or statistical segmentation of data based on its performance with regard to time. WIREs Comput Stat 2012 doi: 10.1002/wics.1213 [ABSTRACT FROM AUTHOR]

Local extreme weather events cause more insurance losses overall than large natural disasters. The evidence is provided by long‐term observations of weather and insurance records that are also a foundation for the majority of insurance products covering weather related damages. The insurers around the world are concerned, however, that the past records used to assess and price the risks underestimate the risk and incurred losses in recent years. The growing insurance risks are largely attributed to climate change that brings increasingly more alterations and permanent impact on all aspects of human life and welfare. From floods to hail to excessive wind, adverse atmospheric events are a poignant reminder of how vulnerable our society is across a broad range of threats posed by environmental extremes. Indeed, as climate change effects become more pronounced, we face a new era of risk with increasing weather related damages and losses. This in turn, coupled with challenges of massive climatic data, requires developing innovative analytic approaches that transcend traditional disciplinary boundaries of statistical, actuarial and environmental sciences. Nevertheless, the multidisciplinary nature of climate risk assessment and its impact on insurance is often overlooked and neglected. We highlight the most recent developments and interdisciplinary perspectives on diverse statistical and machine learning methodology for modeling and assessing climate risk in agricultural and home insurances, with a particular focus on noncatastrophic events. This article is categorized under:Applications of Computational Statistics > Computational Climate Change and Numerical Weather ForecastingStatistical and Graphical Methods of Data Analysis > Multivariate AnalysisData: Types and Structure > Massive Data [ABSTRACT FROM AUTHOR]